Associating multiple data sources into a web-accessible framework

ABSTRACT

Systems, methods, and computer-readable media for associating multiple data sources into a web-accessible framework. Health data is received from multiple data sources and is used to populate a framework comprising at least one topic focused data mart. Each topic focused data mart has a common structure and is associated with a web service providing standard features supported by each topic focused data mart and custom features specific to a topic associated with each topic focused data mart. In various embodiments, demographic information is received from a clinician and is utilized to present context-specific data derived from the topic focused data mart.

BACKGROUND

The advent of powerful servers, large-scale data storage and otherinformation infrastructure has spurred the development of advanced datawarehousing and data mining applications. Structured query language(SQL) engines, on-line analytical processing (OLAP) databases andinexpensive large disk arrays have, for instance, been harnessed infinancial, scientific, medical, and other fields to capture and analyzevast streams of transactional, experimental, and other data. The miningof that data can reveal sales trends, weather patterns, diseaseepidemiology and other patterns not evident from more limited orsmaller-scale analysis.

In the case of health-related data management, the task of receiving,conditioning, and analyzing large quantities of clinical information inreal-time is particularly challenging. The sources of health-relateddata for an organization to better provide context, for instance,include large data warehouses, such as statehealthfacts.org, data.gov,and clinical trials.gov, each of which may store many terabytes of data.The variety and depth of data represented in these data warehousesimpedes the performance of typical querying strategies. Although thereis widespread belief that data in these data warehouses can be used toinform health care and health at the personal and institutional levels,they are not structured to support real-time web access or implementedin a manner sufficiently robust to support health care delivery.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Embodiments of the present invention relate to systems, methods, andcomputer-readable media for, among other things, associating multipledata sources into a web-accessible framework. In one embodiment,computer storage media having computer-executable instructions embodiedthereon, that when executed, perform a method for associating multipledata sources into a web-accessible framework. Health data is receivedfrom multiple data sources. A framework comprising at least one topicfocused data mart is populated. Each topic focused data mart receives atleast a portion of the data from the multiple data sources and eachtopic focused data mart has a common structure. Each topic focused datamart is associated with a web service providing standard featuressupported by each topic focused data mart and custom features specificto a topic associated with each topic focused data mart.

In another embodiment of the present invention, computer storage mediahaving computer-executable instructions embodied thereon, that whenexecuted, perform a method for presenting context-specific data derivedfrom one or more web-accessible, topic focused data marts. Health datais received from at least one data source. A topic focused data mart ispopulated with at least a portion of data received from the at least onedata source. The topic focused data mart is associated with a webservice. Demographic information is received from a clinician. Theclinician is presented with context-specific data derived from the topicfocused data mart and based on the demographic information.

In yet another embodiment of the present invention, a computer systemfor associating multiple data sources into a framework comprises aprocessor couple to a computer storage medium, the computer storagemedium having stored thereon a plurality of computer software componentsexecutable by the processor. A health data component receives healthdata from multiple sources. A framework component populates a frameworkcomprising at least one topic focused data mart, each topic focused datamart having at least a portion of the data received from the multipledata sources and each topic focused data mart having a common structure.A web service component associates each topic focused data mart with aweb service providing standard features supported by each topic focuseddata mart and custom features specific to a topic associated with eachtopic focused data mart. A listener component provides a listener forupdating at least a portion of the health data. A request componentreceives a request for at least a portion of the health data. Aconnection component enables connection points to an Electronic HealthRecord (EHR), a Public Health Record (PHR), or an administrativedashboard. A demographic component receives demographic information froma clinician. A presentation component presents the clinician withcontext-specific data derived from one or more topic focused data martsand based on the demographic information.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in detail below with reference to theattached drawings figures, wherein:

FIG. 1 is a block diagram of an exemplary computing system suitable foruse in implementing embodiments of the present invention;

FIG. 2 schematically shows a network environment suitable for performingembodiments of the invention;

FIG. 3 is a flow diagram showing a method for associating multiple datasources into a framework, in accordance with an embodiment of thepresent invention; and

FIG. 4 is a flow diagram showing a method for presentingcontext-specific data derived from one or more web-accessible, topicfocused data marts, in accordance with an embodiment of the presentinvention.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent components of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

Embodiments of the present invention can empower a patient's,clinician's, or organization's ability to harness pertinent data acrossmultiple data warehouses to access, in real-time, topic focused datamarts efficiently and with limited knowledge of the underlying datastructure. Embodiments present advantages over other systems which arelimited to querying data from static, or outdated, content byassociating each topic focused data mart with a web service.

A topic focused data mart refers to a subset of data from one or moredata warehouses, typically oriented to a specific purpose or major datasubject. Topic focused data marts are analytical data stores designed tofocus on a specific community or purpose within an organization. Topicfocused data marts are often derived from subsets of data in variousdata warehouses.

Therapeutic drug monitoring (TDM) refers to testing of medication levelsto determine whether a patient is receiving an optimal dosage of themedication. Because there is complex interaction between patientcharacteristics and drug properties, the rate at which medicationsaccumulate in the body varies considerably. TDM is a type ofpharmacokinetic assessment to determine concentration of medication inpatients' bodies through laboratory tests, particularly serum bloodlevels. For example, drug metabolism is influenced by factors such asweight, kidney/liver functioning, drug half-life, repeated doses, androutes of administration. Although standard dosage recommendationsexist, clinicians often adjust and individualize treatment.

Having briefly described embodiments of the present invention, anexemplary operating environment suitable for use in implementingembodiments of the present invention is described below. Referring toFIG. 1 an exemplary computing environment with which embodiments of thepresent invention may be implemented is illustrated and designatedgenerally as reference numeral 20. It will be understood and appreciatedby those of ordinary skill in the art that the illustrated medicalinformation computing system environment 20 is merely an example of onesuitable computing environment and is not intended to suggest anylimitation as to the scope of use or functionality of the invention.Neither should the medical information computing system environment 20be interpreted as having any dependency or requirement relating to anysingle component or combination of components illustrated therein.

Embodiments of the present invention may be operational with numerousother general purpose or special purpose computing system environmentsor configurations. Examples of well-known computing systems,environments, and/or configurations that may be suitable for use withthe present invention include, by way of example only, personalcomputers, server computers, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of theabove-mentioned systems or devices, and the like.

Embodiments of the present invention may be described in the generalcontext of computer-executable instructions, such as program modules,being executed by a computer. Generally, program modules include, butare not limited to, routines, programs, objects, components, and datastructures that perform particular tasks or implement particularabstract data types. Embodiments of the present invention may also bepracticed in distributed computing environments where tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules may be located in local and/or remote computer storage mediaincluding, by way of example only, memory storage devices.

With continued reference to FIG. 1, the exemplary computing environment20 includes a general purpose computing device in the form of a server22. Components of the server 22 may include, without limitation, aprocessing unit, internal system memory, and a suitable system bus forcoupling various system components, including database cluster 24, withthe server 22. The system bus may be any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, and a local bus, using any of a variety of bus architectures. Byway of example, and not limitation, such architectures include IndustryStandard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus,Enhanced ISA (EISA) bus, Video Electronic Standards Association (VESA)local bus, and Peripheral Component Interconnect (PCI) bus, also knownas Mezzanine bus.

The server 22 typically includes, or has access to, a variety ofcomputer readable media, for instance, database cluster 24. Computerreadable media can be any available media that may be accessed by server22, and includes volatile and nonvolatile media, as well as removableand non-removable media. By way of example, and not limitation, computerreadable media may include computer storage media and communicationmedia. Computer storage media may include, without limitation, volatileand nonvolatile media, as well as removable and nonremovable mediaimplemented in any method or technology for storage of information, suchas computer readable instructions, data structures, program modules, orother data. In this regard, computer storage media may include, but isnot limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVDs) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage, orother magnetic storage device, or any other medium which can be used tostore the desired information and which may be accessed by the server22. Communication media typically embodies computer readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. As usedherein, the term “modulated data signal” refers to a signal that has oneor more of its attributes set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared, and other wireless media. Combinations of any of the abovealso may be included within the scope of computer readable media.

The computer storage media discussed above and illustrated in FIG. 1,including database cluster 24, provide storage of computer readableinstructions, data structures, program modules, and other data for theserver 22.

The server 22 may operate in a computer network 26 using logicalconnections to one or more remote computers 28. Remote computers 28 maybe located at a variety of locations in a medical or researchenvironment, for example, but not limited to, clinical laboratories,hospitals and other inpatient settings, veterinary environments,ambulatory settings, medical billing and financial offices, hospitaladministration settings, home health care environments, and clinicians'offices. Clinicians may include, but are not limited to, a treatingphysician or physicians, specialists such as surgeons, radiologists,cardiologists, and oncologists, emergency medical technicians,physicians' assistants, nurse practitioners, nurses, nurses' aides,pharmacists, dieticians, microbiologists, laboratory experts, geneticcounselors, researchers, veterinarians, students, and the like. Theremote computers 28 may also be physically located in non-traditionalmedical care environments so that the entire health care community maybe capable of integration on the network. The remote computers 28 may bepersonal computers, servers, routers, network PCs, peer devices, othercommon network nodes, or the like, and may include some or all of thecomponents described above in relation to the server 22. The devices canbe personal digital assistants or other like devices.

Exemplary computer networks 26 may include, without limitation, localarea networks (LANs) and/or wide area networks (WANs). Such networkingenvironments are commonplace in offices, enterprise-wide computernetworks, intranets, and the Internet. When utilized in a WAN networkingenvironment, the server 22 may include a modem or other means forestablishing communications over the WAN, such as the Internet. In anetworked environment, program modules or portions thereof may be storedin the server 22, in the database cluster 24, or on any of the remotecomputers 28. For example, and not by way of limitation, variousapplication programs may reside on the memory associated with any one ormore of the remote computers 28. It will be appreciated by those ofordinary skill in the art that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers (e.g., server 22 and remote computers 28) may be utilized.

In operation, a user may enter commands and information into the server22 or convey the commands and information to the server 22 via one ormore of the remote computers 28 through input devices, such as akeyboard, a pointing device (commonly referred to as a mouse), atrackball, or a touch pad. Other input devices may include, withoutlimitation, microphones, satellite dishes, scanners, or the like.Commands and information may also be sent directly from a remotehealthcare device to the server 22. In addition to a monitor, the server22 and/or remote computers 28 may include other peripheral outputdevices, such as speakers and a printer.

Although many other internal components of the server 22 and the remotecomputers 28 are not shown, those of ordinary skill in the art willappreciate that such components and their interconnections are wellknown. Accordingly, additional details concerning the internalconstruction of the server 22 and the remote computers 28 are notfurther disclosed herein.

Referring now to FIG. 2, a block diagram is provided illustrating anexemplary system 200 in which a framework engine 220 is shown interfacedwith an information system 210 in accordance with an embodiment of thepresent invention. The information system 210 may be a comprehensivecomputing system within a clinical environment similar to the exemplarycomputing system 20 discussed above with reference to FIG. 1.

The information system 210 may be connected via the network to one ormore data warehouses 205, storing various forms of data collected bythird parties such as statehealthfacts.org, data.gov, and clinicaltrials.gov. The data warehouses store data collected from a variety ofsources and comprising many data elements. An internal data warehousemay be utilized by information system 210 and may be stored within theinformation system 210 or external to the information system 210.

The framework engine 220 is generally configured to associate multipledata sources into a framework, reducing the work effort required toimplement application programming interfaces (APIs) for each topicspecific data mart. Each topic specific data mart is associated with aweb service which provides standard features supported for all topicspecific data marts. These standard features include security andrequest/reply interactions. Features specific to the topic of the nodeare also provided. The topic specific data marts are accessible viastandard web services calls, thereby enabling a wide variety ofconnection points to the EHR, PHR, or administrative dashboard. Theability to evaluate benchmarks across data sources is also enhanced byutilizing web services calls.

In some embodiments, the data is a blend of proprietary and publicallyavailable data to better provide context for a patient seeking healthcare or a person seeking to improve their health. In other embodiments,the data supports administrative and patient specific scenarios using acommon framework. In one embodiment, a web services layer allows thefront end implementation to be separate from the back end data layer,promoting reuse of the service for multiple venues.

As shown in FIG. 2, the framework engine 220 includes health datacomponent 221, framework component 222, web service component 223,listener component 224, request component 225, connection component 226,demographic component 227, and presentation component 228. In oneembodiment, the framework engine 220 also includes decision supportengine component 229.

Health data component 221 receives health data from multiple sources. Invarious embodiments, the health data is public health data includingaverage birth weights, a list of clinical trials, comparison metricssuch as length of stay by condition, TDM levels, and air qualityresults. In one embodiment, the health data is internal data collectedby a health care facility or institution. In other embodiments, thehealth data is stored in large publically available data warehouses,such as statehealthfacts.org, data.gov, and clinical trials.gov. Inother embodiments, the health data is stored in proprietary datawarehouses. In one embodiment, health data component 221 derives newdata from the health data. For example, health data component 221 mayreceive discrete health data and summarize it at various time levels(e.g., by day, week, month, etc.).

Framework component 222 populates a framework comprising at least onetopic focused data mart. Each topic focused data mart comprises at leasta portion of the data received from the multiple data sources. Becauseeach topic focused data mart represents a subset of the data that isavailable, the topic focused data marts can be queried much moreefficiently than a larger data warehouse. Each topic focused data martalso has a common structure allowing disparate data to be accessed bysimilar front-end methods. Such a common structure also facilitates areal-time web access that allows the front end implementation to beseparated from the back end data layer and promotes reuse by multiplevenues. Each topic focused data mart, in one embodiment, is accessiblevia the network similarly to the data warehouses 205. In thisembodiment, the framework component 222 populates the framework via thenetwork. In another embodiment, each topic focused data mart is storedwithin the framework engine 220.

Web service component 223 associates each topic focused data mart with aweb service. The web service provides standard features supported byeach topic focused data mart. Such standard features may includesecurity and request/reply interactions. In addition, the web serviceprovides custom features specific to a topic associated with each topicfocused data mart. The use of web services calls to access each topicfocused data mart further enables a wide variety of connection points tothe EHR or PHR, including administrative dashboards or any applicationsdesigned to support a variety of calls.

Listener component 224 provides a listener for updating at least aportion of the health data. The listener allows each topic focused datamart to maintain current data. Querying the larger, public datawarehouse would require significant overhead and is not a practicalsolution for support real-time access. However, because each topicfocused data mart is streamlined with data relevant to the given topic,the listener enables each topic focused data mart to maintain up-to datedata while also supporting real-time access.

Request component 225 receives a request for at least a portion of thehealth data. For example, a clinician may be treating a patient takingcertain medications. The clinician may want to verify the patient isreceiving an optimal dosage of the medication. Laboratory results arecompared with health data related to the medication that indicatewhether the patient is receiving a dosage that is likely to be effectiveand not have an increased risk of adverse events.

Connection component 226 enables, in various embodiments, connectionpoints to an EHR, PHR, administrative dashboard, or any application thatcan communicate with a web service. The connection points allow the EHRor PHR, in one embodiment, to communicate with the web service totransmit demographic information and receive context-specific data froma topic focused data mart. In one embodiment, an EMR associated with apatient is automatically populated with the context-specific data. Thisallows a clinician treating a patient to view information associatedwith the health data that is relevant to the patient, without requiringthe clinician to query the topic focused data mart or enter anydemographic information. Rather, the demographic information is pulledfrom the EMR and is used to query the topic focused data mart and thecontext-specific data is automatically received by and presented in theEMR.

Demographic component 227 receives demographic information from aclinician. In one embodiment, the demographic information isautomatically received from the EHR associated with a patient. Thedemographic information may include geographical information,institution information, information related to characteristics of thepatient, and the like.

Presentation component 228 presents the clinician with context-specificdata derived from one or more topic focused data marts and thedemographic information. In one embodiment, the context-specific data isaverage birth weights for the county, state, and/or country. In oneembodiment, the context-specific data is a list of clinical trials forwhich a patient is eligible. In one embodiment, the context-specificdata is length of stay by condition. In one embodiment, thecontext-specific data is TDM levels in serum across institutions. In oneembodiment, the context-specific data is environmental information. Inone embodiment, the context-specific data is epidemic information.

Decision support engine component 229, in one embodiment, enables adecision support engine to send alerts associated with thecontext-specific data. For example, a topic focused data mart maysummarize air quality results from public sources in a region. Thedecision support engine receives this information along with thedemographic information and sends alerts to potentially affected asthmapatients.

With reference to FIG. 3, an exemplary flow diagram representative of amethod for associating multiple data sources into a framework, inaccordance with an embodiment of the present invention is shown andreferenced generally by numeral 300. Method 300 may be implemented usingthe above-described exemplary computing system environment (FIG. 1).Initially, as shown at step 310, health data is received from multipledata sources. As described above, in various embodiments, the healthdata is average birth weights, a list of clinical trials, comparisonmetrics such as length of stay by condition, TDM levels, and air qualityresults. In one embodiment, the health data is internal data collectedby a health care facility or institution. In other embodiments, thehealth data is stored in large publically available data warehouses,such as statehealthfacts.org, data.gov, and clinical trials.gov. Inother embodiments, the health data is stored in proprietary datawarehouses.

At step 320, a framework comprising at least one topic focused data martis populated. Each topic focused data mart comprises at least a portionof the data received from the multiple data sources. Each topic focuseddata mart also has a common structure. As noted above, such a commonstructure facilitates a real-time web access that allows the front endimplementation to be separated from the back end data layer and promotesreuse by multiple venues.

At step 330, each topic focused data mart is associated with a webservice. The web service provides standard features supported by eachtopic focused data mart. Such standard features may include security andrequest/reply interactions. In addition, the web service provides customfeatures specific to a topic associated with each topic focused datamart. As noted above, the use of web services calls to access each topicfocused data mart further enables a wide variety of connection points,in various embodiments, to the EHR, PHR, administrative dashboard, orany application designed to support a variety of calls.

In one embodiment, a request is received for at least a portion of thehealth data. For example, a clinician may have a patient that iseligible for certain clinical trials. The clinician may request datafrom a topic focused data mart that includes such information populatedfrom at least a portion of the health data received from multiplesources.

In one embodiment, a listener is provided for updating at least aportion of the health data. As noted above, the listener allows eachtopic focused data mart to maintain current data. Querying the larger,public data warehouse would require significant overhead and is not apractical solution for support real-time access. However, because eachtopic focused data mart is streamlined with data relevant to the giventopic, the listener enables each topic focused data mart to maintainup-to date data while also supporting real-time access.

In one embodiment, reporting of events identified above upper limit ofnormal (ULN) thresholds that occur within and/or across facilities isenabled. For example, a clinician may prescribe a certain medication toa patient. If the medication level is above an optimal threshold that isassociated with an increased risk of adverse events, the medicationlevel is identified as above ULN. The prescribing clinician may thenadjust the medication level as appropriate. In another embodiment, theeffect of interventions developed to reduce occurrence of the eventsidentified above the ULN thresholds is monitored.

In another embodiment, reporting of events identified below lower limitof normal (LLN) thresholds that occur within and/or across facilities isenabled. For example, if the clinician prescribed a certain medicationto a patient that is below an optimal threshold to achieve the desiredbenefits associated with that medication, the medication level isidentified as below LLN. The prescribing clinician may then adjust themedication level as appropriate. Such reporting promotes appropriate andconsistent drug monitoring to improve health outcomes. In anotherembodiment, the effect of interventions developed to reduce occurrenceof the events identified below the LLN thresholds is monitored.

In one embodiment, the health data is associated with one or more oflifestyles, clinical trials, environmental pollution, countyinspections, drug/device recalls, types of prescriptions otherclinicians prescribe, most common doses, medications, and outcomes.

In practice, a web service queries a topic focused data mart to identifylaboratory results flagged above ULN for inpatients that have beenadmitted more than 48 hours. The flagged laboratory results are used tocreate TDM data reports that describe the percentage of ULN events at asingle facility over a 12-month period and relative to aggregatenational results. Participating facilities can access the TDM datareports to support benchmarking and quality improvement initiatives.

In another example, a clinical pharmacy coordinator is responsible forcoordinating pharmacy services to ensure that the pharmacy providesoptimal clinical services. Upon noticing a high occurrence of ULN eventsat the facility, the clinical pharmacy coordinator develops qualityimprovement programs (i.e., interventions) to address the findings. TheTDM reports are used to monitor the effect of the interventions overtime and modify the programs accordingly. As can be appreciated, the TDMreports, and the overall web service, can reduce hospital safety eventsand improve health outcomes. In various embodiments, carbamazepine,digoxin, phenytoin, gentamicin, tobramycin, vancomycin, lidocaine,lithium, valproic acid, methotrexate levels are monitored. In otherembodiments, antibiotics levels and ambulatory care specifics aremonitored.

With reference to FIG. 4, an exemplary flow diagram representative of amethod for presenting context-specific data derived from one or moreweb-accessible, topic focused data marts, in accordance with anembodiment of the present invention is shown and referenced generally bynumeral 400. Method 400 may be implemented using the above-describedexemplary computing system environment (FIG. 1). Initially, as shown atstep 410, health data is received from at least one data source. Asdescribed above, in various embodiments, the health data is averagebirth weights, a list of clinical trials, comparison metrics such aslength of stay by condition, TDM levels, and air quality results. In oneembodiment, the health data is internal data collected by a health carefacility or institution. In other embodiments, the health data is storedin large publically available data warehouses, such asstatehealthfacts.org, data.gov, and clinical trials.gov. In otherembodiments, the health data is stored in proprietary data warehouses.At step 420, a topic focused data mart, having at least a portion of thedata received from the at least one data source is populated.

The topic focused data mart, at step 430, is associated with a webservice. The web service provides standard features supported by eachtopic focused data mart. Such standard features may include security andrequest/reply interactions. In addition, the web service provides customfeatures specific to a topic associated with each topic focused datamart. As noted above, the use of web services calls to access each topicfocused data mart further enables a wide variety of connection points,in various embodiments, to the EHR, PHR, administrative dashboard, orany application designed to support a variety of calls.

Demographic information is received from a clinician at step 440. Thedemographic information may include geographical information,institution information, information related to characteristics of thepatient, and the like.

The clinician is presented, at step 450, with context-specific dataderived from the topic focused data mart and based on the demographicinformation. In one embodiment, the context-specific data is averagebirth weights for the county, state, and/or country. In one embodiment,the context-specific data is a list of clinical trials for which apatient is eligible. In one embodiment, the context-specific data islength of stay by condition. In one embodiment, the context-specificdata is TDM levels in serum across institutions. In one embodiment, thecontext-specific data is environmental information. In one embodiment,the context-specific data is epidemic information.

In one embodiment, a decision support engine is enabled to send alertsassociated with the context-specific data. In one embodiment, the alertsprovide the clinician with a warning that a certain threshold has beenexceeded. In another embodiment, the alerts provide additionalinformation associated with a patient. In another embodiment, the alertsprovide the clinician with information associated with the treatment ofa patient. In another embodiment, the alerts provide the clinician withinformation associated with clinical trial for which a patient iseligible. In another embodiment, the alerts provide a patient withinformation. In one embodiment, the alerts provide information relatedto a public health situation, such as a disease outbreak orenvironmental conditions.

In one embodiment, an EMR associated with a patient is automaticallypopulated with the context-specific data. This allows a cliniciantreating a patient to view information associated with the health datathat is relevant to the patient, without requiring the clinician toquery the topic focused data mart or enter any demographic information.Rather, the demographic information is pulled from the EMR and is usedto query the topic focused data mart and the context-specific data isautomatically received by and presented in the EMR.

As can be understood, the present invention provides systems, methods,and user interfaces for associating multiple data sources into aweb-accessible framework. The present invention has been described inrelation to particular embodiments, which are intended in all respectsto be illustrative rather than restrictive. Alternative embodiments willbecome apparent to those of ordinary skill in the art to which thepresent invention pertains without departing from its scope.

From the foregoing, it will be seen that this invention is one welladapted to attain all the ends and objects set forth above, togetherwith other advantages which are obvious and inherent to the system andmethod. It will be understood that certain features and subcombinationsare of utility and may be employed without reference to other featuresand subcombinations. This is contemplated and within the scope of theclaims.

What is claimed is:
 1. Computer storage media having computer-executableinstructions embodied thereon, that when executed, perform a method forassociating multiple data sources into a framework, the methodcomprising: receiving health data from multiple data sources; populatinga framework comprising at least one topic focused data mart, each topicfocused data mart having at least a portion of the data received fromthe multiple data sources and each topic focused data mart having acommon structure; and associating each topic focused data mart with aweb service providing standard features supported by each topic focuseddata mart and custom features specific to a topic associated with eachtopic focused data mart.
 2. The media of claim 1, further comprisingreceiving a request for at least a portion of the health data.
 3. Themedia of claim 1, further comprising enabling connection points to anElectronic Health Record (EHR), a Public Health Record (PHR),administrative dashboard, or any application that can communicate with aweb service.
 4. The media of claim 1, further comprising providing alistener for updating at least a portion of the health data.
 5. Themedia of claim 2, further comprising enabling reporting of eventsidentified above upper limit of normal (ULN) thresholds that occurwithin and/or across facilities.
 6. The media of claim 2, furthercomprising enabling reporting of events identified below lower limit ofnormal (LLN) thresholds that occur within and/or across facilities. 7.The media of claim 5, further comprising monitoring an effect ofinterventions developed to reduce occurrence of the events identifiedabove the ULN thresholds.
 8. The media of claim 6, further comprisingmonitoring an effect of interventions developed to reduce occurrence ofthe events identified below the LLN thresholds
 9. The media of claim 1,wherein the health data is associated with one or more of lifestyles,clinical trials, environmental pollution, county inspections,drug/device recalls, types of prescriptions other clinicians prescribe,most common doses, medications, and outcomes.
 10. Computer storage mediahaving computer-executable instructions embodied thereon, that whenexecuted, perform a method for presenting a web-accessible topic focuseddata mart, the method comprising: receiving health data from at leastone data source; populating a topic focused data mart having at least aportion of the health data received from the at least one data source;associating the topic focused data mart with a web service; receivingdemographic information from a clinician; and presenting the clinicianwith context-specific data derived from the topic focused data mart andbased on the demographic information.
 11. The media of claim 10, whereinthe context-specific data is average birth weights for the country,state, and country.
 12. The media of claim 10, wherein thecontext-specific data is a list of clinical trials for which a patientis eligible.
 13. The media of claim 10, wherein the context-specificdata is length of stay by condition.
 14. The media of claim 10, whereinthe context-specific data is therapeutic drug monitoring levels in serumacross institutions.
 15. The media of claim 10, wherein thecontext-specific data is environmental information.
 16. The media ofclaim 10, wherein the context-specific data is epidemic information. 17.The media of claim 10, further comprising enabling a decision supportengine to send alerts associated with the context-specific data.
 18. Themedia of claim 10, further comprising automatically populating an EMRassociated with a patient with the context-specific data.
 19. A computersystem for associating multiple data sources into a framework, thecomputer system comprising a processor coupled to a computer storagemedium, the computer storage medium having stored thereon a plurality ofcomputer software components executable by the processor, the computersoftware components comprising: a health data component for receivinghealth data from multiple sources; a framework component for populatinga framework comprising at least one topic focused data mart, each topicfocused data mart having at least a portion of the health data receivedfrom the multiple data sources and each topic focused data mart having acommon structure; an web service component for associating each topicfocused data mart with a web service providing standard featuressupported by each topic focused data mart and custom features specificto a topic associated with each topic focused data mart; a listenercomponent for providing a listener for updating at least a portion ofthe health data; a request component for receiving a request for atleast a portion of the health data; a connection component for enablingconnection points to an Electronic Health Record (EHR), a Public HealthRecord (PHR), an administrative dashboard, or any application that cancommunicate with a web service; a demographic component for receivingdemographic information from a clinician; and a presentation componentfor presenting the clinician with context-specific data derived from oneor more topic focused data marts and based on the demographicinformation.
 20. The system of claim 19, further comprising a decisionsupport engine component for enabling a decision support engine to sendalerts associated with the context-specific data.